To comprehensively showcase MetNet-2's performance, we consider other training modes for MetNet-2 that, contrary to the default MetNet-2, make use of the outcome of NWP's atmospheric simulation. MetNet-2 Postprocess takes as an input NWP's forecast for a given lead time along with static location, altitude and time features and learns to map NWP's forecast as closely as possible to the ground truth. It also learns to correct for any systematic biases in NWP's forecast and makes the forecast probabilistic. MetNet-2 Hybrid learns to extract information from all the available inputs, including the twelve hourly forecasts from the NWP as well as the radar and assimilation inputs used in the default MetNet-2. Whereas an NWP is at its core a deterministic model, MetNet-2 and the variants are probabilistic at their core. An NWP can be made probabilistic through ensembling or through statistical post-processing [1]} such as, for example, the HREF model that produces an ensemble from 10 deterministic NWP members. Our own MetNet-2 Postprocess variant can be thought of as a probabilistic post-processing model for the deterministic NWP.
Figure REF  summarizes the framework.
